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Fig. 7 | BMC Bioinformatics

Fig. 7

From: Comprehensive study of semi-supervised learning for DNA methylation-based supervised classification of central nervous system tumors

Fig. 7

Prediction performance of random forest (RF) and neural net (NN) classifiers at sub-class level when trained with different combination of reference samples and semi-supervised (SS) predicted labeled samples. A Balanced accuracy of RF and NN. B Proportion (left panel) and count (right panel) of high (≥ 10 samples, red) and low (< 10 samples; green) frequency referent labels, high confident (calibrated SSL scores ≥ 0.8, HC) SS labels with high frequency (blue) and low frequency (orange) subclasses, and low confident (calibrated SSL scores < 0.8, LC) SS labels in high frequency (yellow) and low frequency (purple) subclasses. Asterisks indicate statistically significant difference performed by Tukey Honest Significant Difference test at 0.05 alpha level

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BMC Bioinformatics

ISSN: 1471-2105

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